1. Overview of Project

Insert Abstract

2. Introduction of the file

The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity,including general, driving, financial, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, trajectory plots, the meta-analysis results. We tested individual predictors that are not included in the simple trajectory model in meta regression: country, continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert link)

3. Overview of panel data

3.1. The number of participants

3.2. Histogram of age distributions (all observations)

3.3. Histograms and Density Plots for every panel

SOEP

Age distribution

Risk density

HRS

Age distribution

Risk density

SAVE

Age distribution

Risk density

DNB

Age distribution

Risk density

PHF

Age distribution

Risk density

SHARE_Austria

Age distribution

Risk density

SHARE_Germany

Age distribution

Risk density

SHARE_Sweden

Age distribution

Risk density

SHARE_Netherlands

Age distribution

Risk density

SHARE_Spain

Age distribution

Risk density

SHARE_Italy

Age distribution

Risk density

SHARE_France

Age distribution

Risk density

SHARE_Denmark

Age distribution

Risk density

SHARE_Switzerland

Age distribution

Risk density

SHARE_Belgium

Age distribution

Risk density

SHARE_Israel

Age distribution

Risk density

SHARE_Czech_Republic

Age distribution

Risk density

SHARE_Slovenia

Age distribution

Risk density

SHARE_Estonia

Age distribution

Risk density

Usoc

Age distribution

Risk density

GCOE_Japan

Age distribution

Risk density

GCOE_USA

Age distribution

Risk density

HILDA

Age distribution

Risk density

LIKS

Age distribution

Risk density

4. Multi-level model results and Meta-analysis

4.1. General risk-taking

Intercept only model

Models results
Meta analysis of ICC’s results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.9707   -7.9414   -3.9414   -4.3579    0.0586   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value):      0.1247
## I^2 (total heterogeneity / total variability):   99.83%
## H^2 (total variability / sampling variability):  605.99
## 
## Test for Heterogeneity:
## Q(df = 6) = 3997.8164, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.4331  0.0473  9.1579  <.0001  0.3404  0.5259  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0346
## I^2 (total heterogeneity / total variability):   98.24%
## H^2 (total variability / sampling variability):  56.78
## 
## Test for Heterogeneity:
## Q(df = 6) = 251.5222, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0777  0.0135  -5.7739  <.0001  -0.1041  -0.0514  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0347
## I^2 (total heterogeneity / total variability):   98.19%
## H^2 (total variability / sampling variability):  55.19
## 
## Test for Heterogeneity:
## Q(df = 6) = 231.5468, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0776  0.0135  -5.7524  <.0001  -0.1040  -0.0511  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value):      0.0365
## I^2 (total heterogeneity / total variability):   98.40%
## H^2 (total variability / sampling variability):  62.49
## 
## Test for Heterogeneity:
## Q(df = 6) = 242.4521, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0785  0.0141  -5.5522  <.0001  -0.1062  -0.0508  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0074)
## tau (square root of estimated tau^2 value):      0.1097
## I^2 (total heterogeneity / total variability):   98.28%
## H^2 (total variability / sampling variability):  57.98
## 
## Test for Heterogeneity:
## Q(df = 6) = 296.8534, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2353  0.0427  -5.5097  <.0001  -0.3190  -0.1516  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender interaction model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0010)
## tau (square root of estimated tau^2 value):      0.0398
## I^2 (total heterogeneity / total variability):   97.14%
## H^2 (total variability / sampling variability):  35.03
## 
## Test for Heterogeneity:
## Q(df = 6) = 113.1256, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0819  0.0157  -5.2151  <.0001  -0.1127  -0.0511  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0074)
## tau (square root of estimated tau^2 value):      0.1096
## I^2 (total heterogeneity / total variability):   97.73%
## H^2 (total variability / sampling variability):  43.99
## 
## Test for Heterogeneity:
## Q(df = 6) = 276.5847, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2268  0.0428  -5.3000  <.0001  -0.3107  -0.1429  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age \(\times\) gender effect results
## 
## Random-Effects Model (k = 7; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value):      0.0201
## I^2 (total heterogeneity / total variability):   82.50%
## H^2 (total variability / sampling variability):  5.71
## 
## Test for Heterogeneity:
## Q(df = 6) = 35.8767, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0070  0.0091  0.7720  0.4401  -0.0108  0.0248    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Quadratic model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0211
## I^2 (total heterogeneity / total variability):   96.03%
## H^2 (total variability / sampling variability):  25.16
## 
## Test for Heterogeneity:
## Q(df = 3) = 67.8368, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0836  0.0109  -7.6376  <.0001  -0.1051  -0.0621  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0122
## I^2 (total heterogeneity / total variability):   96.98%
## H^2 (total variability / sampling variability):  33.15
## 
## Test for Heterogeneity:
## Q(df = 3) = 149.9145, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0016  0.0064  -0.2488  0.8035  -0.0141  0.0109    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Quadratic with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value):      0.0233
## I^2 (total heterogeneity / total variability):   96.79%
## H^2 (total variability / sampling variability):  31.16
## 
## Test for Heterogeneity:
## Q(df = 3) = 76.0587, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0840  0.0120  -7.0032  <.0001  -0.1075  -0.0605  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0115
## I^2 (total heterogeneity / total variability):   96.68%
## H^2 (total variability / sampling variability):  30.15
## 
## Test for Heterogeneity:
## Q(df = 3) = 139.5422, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0018  0.0060  -0.2973  0.7662  -0.0136  0.0100    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 4; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0083 (SE = 0.0070)
## tau (square root of estimated tau^2 value):      0.0909
## I^2 (total heterogeneity / total variability):   97.91%
## H^2 (total variability / sampling variability):  47.86
## 
## Test for Heterogeneity:
## Q(df = 3) = 95.8378, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2858  0.0462  -6.1816  <.0001  -0.3764  -0.1952  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

4.2. Driving risk-taking

Intercept only model

Models results
Meta analysis of ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.5503   -5.1006   -1.1006   -3.7143   10.8994   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0045 (SE = 0.0046)
## tau (square root of estimated tau^2 value):      0.0672
## I^2 (total heterogeneity / total variability):   98.65%
## H^2 (total variability / sampling variability):  73.93
## 
## Test for Heterogeneity:
## Q(df = 2) = 205.8566, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4641  0.0392  11.8491  <.0001  0.3873  0.5409  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0005)
## tau (square root of estimated tau^2 value):      0.0202
## I^2 (total heterogeneity / total variability):   88.84%
## H^2 (total variability / sampling variability):  8.96
## 
## Test for Heterogeneity:
## Q(df = 2) = 24.2253, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1024  0.0125  -8.1660  <.0001  -0.1270  -0.0778  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0227
## I^2 (total heterogeneity / total variability):   90.89%
## H^2 (total variability / sampling variability):  10.98
## 
## Test for Heterogeneity:
## Q(df = 2) = 30.0259, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1030  0.0139  -7.4184  <.0001  -0.1303  -0.0758  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0228
## I^2 (total heterogeneity / total variability):   91.39%
## H^2 (total variability / sampling variability):  11.61
## 
## Test for Heterogeneity:
## Q(df = 2) = 31.7710, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1099  0.0139  -7.8912  <.0001  -0.1371  -0.0826  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0561
## I^2 (total heterogeneity / total variability):   89.18%
## H^2 (total variability / sampling variability):  9.24
## 
## Test for Heterogeneity:
## Q(df = 2) = 16.6401, p-val = 0.0002
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3902  0.0345  -11.3253  <.0001  -0.4578  -0.3227  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender interaction model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value):      0.0121
## I^2 (total heterogeneity / total variability):   59.11%
## H^2 (total variability / sampling variability):  2.45
## 
## Test for Heterogeneity:
## Q(df = 2) = 5.0592, p-val = 0.0797
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1259  0.0092  -13.6449  <.0001  -0.1440  -0.1078  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value):      0.0473
## I^2 (total heterogeneity / total variability):   78.07%
## H^2 (total variability / sampling variability):  4.56
## 
## Test for Heterogeneity:
## Q(df = 2) = 10.2374, p-val = 0.0060
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3987  0.0322  -12.3785  <.0001  -0.4619  -0.3356  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age \(\times\) gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0009)
## tau (square root of estimated tau^2 value):      0.0255
## I^2 (total heterogeneity / total variability):   76.61%
## H^2 (total variability / sampling variability):  4.27
## 
## Test for Heterogeneity:
## Q(df = 2) = 9.0295, p-val = 0.0109
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0264  0.0172  1.5409  0.1233  -0.0072  0.0601    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

4.3. Financial risk-taking

Intercept only model

Models results
Meta analysis of ICC’s results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##  14.3922  -28.7845  -24.7845  -22.8956  -24.0345   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0041)
## tau (square root of estimated tau^2 value):      0.1103
## I^2 (total heterogeneity / total variability):   99.42%
## H^2 (total variability / sampling variability):  171.74
## 
## Test for Heterogeneity:
## Q(df = 19) = 2251.6232, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.3622  0.0253  14.3385  <.0001  0.3127  0.4117  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0428
## I^2 (total heterogeneity / total variability):   97.13%
## H^2 (total variability / sampling variability):  34.88
## 
## Test for Heterogeneity:
## Q(df = 19) = 448.9503, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1086  0.0101  -10.6991  <.0001  -0.1285  -0.0887  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value):      0.0433
## I^2 (total heterogeneity / total variability):   96.89%
## H^2 (total variability / sampling variability):  32.15
## 
## Test for Heterogeneity:
## Q(df = 19) = 411.3669, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1094  0.0102  -10.7017  <.0001  -0.1294  -0.0893  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0006)
## tau (square root of estimated tau^2 value):      0.0417
## I^2 (total heterogeneity / total variability):   96.74%
## H^2 (total variability / sampling variability):  30.64
## 
## Test for Heterogeneity:
## Q(df = 19) = 416.9017, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1107  0.0099  -11.2353  <.0001  -0.1300  -0.0914  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0041)
## tau (square root of estimated tau^2 value):      0.1086
## I^2 (total heterogeneity / total variability):   96.60%
## H^2 (total variability / sampling variability):  29.41
## 
## Test for Heterogeneity:
## Q(df = 19) = 1042.4280, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.2709  0.0253  -10.7251  <.0001  -0.3204  -0.2214  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender interaction model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0009)
## tau (square root of estimated tau^2 value):      0.0488
## I^2 (total heterogeneity / total variability):   95.13%
## H^2 (total variability / sampling variability):  20.54
## 
## Test for Heterogeneity:
## Q(df = 19) = 307.2837, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1281  0.0119  -10.7695  <.0001  -0.1514  -0.1048  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0143 (SE = 0.0056)
## tau (square root of estimated tau^2 value):      0.1195
## I^2 (total heterogeneity / total variability):   94.95%
## H^2 (total variability / sampling variability):  19.79
## 
## Test for Heterogeneity:
## Q(df = 19) = 906.4073, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3152  0.0296  -10.6553  <.0001  -0.3732  -0.2572  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age \(\times\) gender effect results
## 
## Random-Effects Model (k = 20; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0272
## I^2 (total heterogeneity / total variability):   76.73%
## H^2 (total variability / sampling variability):  4.30
## 
## Test for Heterogeneity:
## Q(df = 19) = 64.7546, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval   ci.lb   ci.ub 
##   0.0302  0.0082  3.6960  0.0002  0.0142  0.0463  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Quadratic model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0039 (SE = 0.0022)
## tau (square root of estimated tau^2 value):      0.0621
## I^2 (total heterogeneity / total variability):   96.91%
## H^2 (total variability / sampling variability):  32.41
## 
## Test for Heterogeneity:
## Q(df = 12) = 87.1316, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0982  0.0205  -4.7957  <.0001  -0.1383  -0.0580  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0161
## I^2 (total heterogeneity / total variability):   91.90%
## H^2 (total variability / sampling variability):  12.35
## 
## Test for Heterogeneity:
## Q(df = 12) = 277.4395, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0000  0.0053  0.0089  0.9929  -0.0104  0.0105    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Quadratic with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0041 (SE = 0.0022)
## tau (square root of estimated tau^2 value):      0.0639
## I^2 (total heterogeneity / total variability):   97.19%
## H^2 (total variability / sampling variability):  35.63
## 
## Test for Heterogeneity:
## Q(df = 12) = 125.4651, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1183  0.0209  -5.6674  <.0001  -0.1592  -0.0774  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age\({ }^{2}\) effect
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0150
## I^2 (total heterogeneity / total variability):   91.05%
## H^2 (total variability / sampling variability):  11.17
## 
## Test for Heterogeneity:
## Q(df = 12) = 297.3119, p-val < .0001
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0047  0.0051  0.9246  0.3552  -0.0052  0.0146    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 13; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0117 (SE = 0.0051)
## tau (square root of estimated tau^2 value):      0.1083
## I^2 (total heterogeneity / total variability):   96.36%
## H^2 (total variability / sampling variability):  27.48
## 
## Test for Heterogeneity:
## Q(df = 12) = 653.3721, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2679  0.0312  -8.5972  <.0001  -0.3289  -0.2068  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

4.4. Recreational risk-taking

Intercept only model

Models results
Meta analysis of ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.2173   -6.4347   -2.4347   -5.0484    9.5653   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0024)
## tau (square root of estimated tau^2 value):      0.0481
## I^2 (total heterogeneity / total variability):   97.89%
## H^2 (total variability / sampling variability):  47.32
## 
## Test for Heterogeneity:
## Q(df = 2) = 132.5530, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4705  0.0281  16.7363  <.0001  0.4154  0.5256  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value):      0.0390
## I^2 (total heterogeneity / total variability):   96.70%
## H^2 (total variability / sampling variability):  30.32
## 
## Test for Heterogeneity:
## Q(df = 2) = 87.7706, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1673  0.0231  -7.2544  <.0001  -0.2126  -0.1221  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0018)
## tau (square root of estimated tau^2 value):      0.0410
## I^2 (total heterogeneity / total variability):   97.03%
## H^2 (total variability / sampling variability):  33.69
## 
## Test for Heterogeneity:
## Q(df = 2) = 100.3378, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1651  0.0242  -6.8287  <.0001  -0.2125  -0.1177  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0015)
## tau (square root of estimated tau^2 value):      0.0382
## I^2 (total heterogeneity / total variability):   96.72%
## H^2 (total variability / sampling variability):  30.47
## 
## Test for Heterogeneity:
## Q(df = 2) = 91.1460, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1701  0.0226  -7.5360  <.0001  -0.2143  -0.1258  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0036 (SE = 0.0041)
## tau (square root of estimated tau^2 value):      0.0604
## I^2 (total heterogeneity / total variability):   90.05%
## H^2 (total variability / sampling variability):  10.05
## 
## Test for Heterogeneity:
## Q(df = 2) = 14.7678, p-val = 0.0006
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3696  0.0369  -10.0119  <.0001  -0.4419  -0.2972  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender interaction model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0022)
## tau (square root of estimated tau^2 value):      0.0451
## I^2 (total heterogeneity / total variability):   95.16%
## H^2 (total variability / sampling variability):  20.65
## 
## Test for Heterogeneity:
## Q(df = 2) = 49.1470, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.1809  0.0271  -6.6739  <.0001  -0.2340  -0.1278  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0005)
## tau (square root of estimated tau^2 value):      0.0036
## I^2 (total heterogeneity / total variability):   1.96%
## H^2 (total variability / sampling variability):  1.02
## 
## Test for Heterogeneity:
## Q(df = 2) = 1.7319, p-val = 0.4207
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.3581  0.0113  -31.6635  <.0001  -0.3803  -0.3359  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age \(\times\) gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0009 (SE = 0.0012)
## tau (square root of estimated tau^2 value):      0.0301
## I^2 (total heterogeneity / total variability):   81.88%
## H^2 (total variability / sampling variability):  5.52
## 
## Test for Heterogeneity:
## Q(df = 2) = 6.7812, p-val = 0.0337
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0260  0.0197  1.3230  0.1858  -0.0125  0.0646    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

4.5. Occupational risk-taking

Intercept only model

Models results
Meta analysis of ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   2.7833   -5.5666   -1.5666   -4.1803   10.4334   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value):      0.0595
## I^2 (total heterogeneity / total variability):   97.97%
## H^2 (total variability / sampling variability):  49.31
## 
## Test for Heterogeneity:
## Q(df = 2) = 106.4098, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.4102  0.0348  11.7962  <.0001  0.3420  0.4783  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0185
## I^2 (total heterogeneity / total variability):   85.54%
## H^2 (total variability / sampling variability):  6.92
## 
## Test for Heterogeneity:
## Q(df = 2) = 18.3813, p-val = 0.0001
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1729  0.0118  -14.6125  <.0001  -0.1961  -0.1497  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0174
## I^2 (total heterogeneity / total variability):   83.99%
## H^2 (total variability / sampling variability):  6.25
## 
## Test for Heterogeneity:
## Q(df = 2) = 16.3125, p-val = 0.0003
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1728  0.0112  -15.3703  <.0001  -0.1948  -0.1508  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0003)
## tau (square root of estimated tau^2 value):      0.0159
## I^2 (total heterogeneity / total variability):   81.61%
## H^2 (total variability / sampling variability):  5.44
## 
## Test for Heterogeneity:
## Q(df = 2) = 13.8301, p-val = 0.0010
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1771  0.0104  -17.0764  <.0001  -0.1975  -0.1568  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0037 (SE = 0.0042)
## tau (square root of estimated tau^2 value):      0.0606
## I^2 (total heterogeneity / total variability):   89.57%
## H^2 (total variability / sampling variability):  9.58
## 
## Test for Heterogeneity:
## Q(df = 2) = 16.7250, p-val = 0.0002
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2738  0.0372  -7.3631  <.0001  -0.3466  -0.2009  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender interaction model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value):      0.0096
## I^2 (total heterogeneity / total variability):   44.44%
## H^2 (total variability / sampling variability):  1.80
## 
## Test for Heterogeneity:
## Q(df = 2) = 4.0755, p-val = 0.1303
## 
## Model Results:
## 
## estimate      se      zval    pval    ci.lb    ci.ub 
##  -0.1772  0.0083  -21.4704  <.0001  -0.1934  -0.1610  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0034 (SE = 0.0044)
## tau (square root of estimated tau^2 value):      0.0579
## I^2 (total heterogeneity / total variability):   84.31%
## H^2 (total variability / sampling variability):  6.37
## 
## Test for Heterogeneity:
## Q(df = 2) = 16.5675, p-val = 0.0003
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2456  0.0382  -6.4262  <.0001  -0.3205  -0.1707  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age \(\times\) gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0018)
## tau (square root of estimated tau^2 value):      0.0383
## I^2 (total heterogeneity / total variability):   86.46%
## H^2 (total variability / sampling variability):  7.38
## 
## Test for Heterogeneity:
## Q(df = 2) = 11.3194, p-val = 0.0035
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb   ci.ub 
##  -0.0074  0.0243  -0.3048  0.7606  -0.0551  0.0403    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

4.6. Health risk-taking

Intercept only model

Models results
Meta analysis of ICC’s results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   3.2235   -6.4470   -2.4470   -5.0607    9.5530   
## 
## tau^2 (estimated amount of total heterogeneity): 0.0022 (SE = 0.0023)
## tau (square root of estimated tau^2 value):      0.0474
## I^2 (total heterogeneity / total variability):   97.11%
## H^2 (total variability / sampling variability):  34.62
## 
## Test for Heterogeneity:
## Q(df = 2) = 72.5854, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   0.3801  0.0279  13.6268  <.0001  0.3255  0.4348  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fixed effect model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0016)
## tau (square root of estimated tau^2 value):      0.0396
## I^2 (total heterogeneity / total variability):   97.21%
## H^2 (total variability / sampling variability):  35.81
## 
## Test for Heterogeneity:
## Q(df = 2) = 92.3561, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0867  0.0234  -3.7114  0.0002  -0.1325  -0.0409  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Linear model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value):      0.0400
## I^2 (total heterogeneity / total variability):   97.27%
## H^2 (total variability / sampling variability):  36.68
## 
## Test for Heterogeneity:
## Q(df = 2) = 95.8506, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0871  0.0236  -3.6934  0.0002  -0.1333  -0.0409  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value):      0.0392
## I^2 (total heterogeneity / total variability):   97.22%
## H^2 (total variability / sampling variability):  35.93
## 
## Test for Heterogeneity:
## Q(df = 2) = 91.0157, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0891  0.0231  -3.8566  0.0001  -0.1344  -0.0438  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0023)
## tau (square root of estimated tau^2 value):      0.0432
## I^2 (total heterogeneity / total variability):   83.54%
## H^2 (total variability / sampling variability):  6.08
## 
## Test for Heterogeneity:
## Q(df = 2) = 13.8841, p-val = 0.0010
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2473  0.0275  -9.0104  <.0001  -0.3012  -0.1935  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

Linear with gender interaction model

Models results
Meta analysis of age effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0014)
## tau (square root of estimated tau^2 value):      0.0359
## I^2 (total heterogeneity / total variability):   93.31%
## H^2 (total variability / sampling variability):  14.95
## 
## Test for Heterogeneity:
## Q(df = 2) = 44.9010, p-val < .0001
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.0953  0.0219  -4.3459  <.0001  -0.1383  -0.0523  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0029 (SE = 0.0039)
## tau (square root of estimated tau^2 value):      0.0539
## I^2 (total heterogeneity / total variability):   83.92%
## H^2 (total variability / sampling variability):  6.22
## 
## Test for Heterogeneity:
## Q(df = 2) = 14.2671, p-val = 0.0008
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  -0.2395  0.0356  -6.7208  <.0001  -0.3094  -0.1697  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Meta analysis of age \(\times\) gender effect results
## 
## Random-Effects Model (k = 3; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0008 (SE = 0.0011)
## tau (square root of estimated tau^2 value):      0.0286
## I^2 (total heterogeneity / total variability):   82.08%
## H^2 (total variability / sampling variability):  5.58
## 
## Test for Heterogeneity:
## Q(df = 2) = 6.6315, p-val = 0.0363
## 
## Model Results:
## 
## estimate      se    zval    pval    ci.lb   ci.ub 
##   0.0183  0.0187  0.9788  0.3277  -0.0184  0.0551    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot

4.7. Social risk-taking

5. Meta analytic summary of slope estimates

Intercept-only model

Fixed effect model

Linear model

Linear with gender model

Linear with gender interaction model

Quadratic model

Quadratic with gender model